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Technical Report

Year Two Progress Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System, Meet Future Demand for City Passenger and Delivery Load/Unload Spaces, and Reduce Energy Consumption

 
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Publication: U.S. Department of Energy
Publication Date: 2021
Summary:

The objectives of this project are to develop and implement a technology solution to support research, development, and demonstration of data processing techniques, models, simulations, a smart phone application, and a visual-confirmation system to:

  1. Reduce delivery vehicle parking seeking behavior by approximately 20% in the pilot test area, by returning current and predicted load/unload space occupancy information to users on a web-based and/or mobile platform, to inform real-time parking decisions
  2. Reduce parcel truck dwell time in pilot test areas in Seattle and Bellevue, Washington, by approximately 30%, thereby increasing productivity of load/unload spaces near common carrier locker systems, and
  3. Improve the transportation network (which includes roads, intersections, warehouses, fulfillment centers, etc.) and commercial firms’ efficiency by increasing curb occupancy rates to roughly 80%, and alley space occupancy rates from 46% to 60% during peak hours, and increasing private loading bay occupancy rates in the afternoon peak times, in the pilot test area.

The project team has designed a 3-year plan to achieve the objectives of this project.

In Year 1, the team developed integrated technologies and finalized the pilot test parameters. This involved finalizing the plan for placing sensory devices and common parcel locker systems on public and private property; issuing the request for proposals; selecting vendors; and gaining approvals necessary to execute the plan. The team also developed techniques to preprocess the data streams from the sensor devices, and began to design the prototype smart phone parking app to display real-time load/unload space availability, as well as the truck load/unload space behavior model.

In Year 2, the team executed the implementation plan:

  • oversaw installation of the in-road sensors, and collecting and processing data,
  • managed installation, marketing and operations of three common locker systems in the pilot test area,
  • tested the prototype smart phone parking app with initial data stream, and
  • developed a truck parking behavior simulation model.
Recommended Citation:
Urban Freight Lab (2021). Year Two Progress Report: Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System.
Paper

Do Commercial Vehicles Cruise for Parking? Empirical Evidence from Seattle

 
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Publication: Transport Policy
Volume: 97
Pages: 26-36
Publication Date: 2020
Summary:

Parking cruising is a well-known phenomenon in passenger transportation, and a significant source of congestion and pollution in urban areas. While urban commercial vehicles are known to travel longer distances and to stop more frequently than passenger vehicles, little is known about their parking cruising behavior, nor how parking infrastructure affect such behavior.

In this study we propose a simple method to quantitatively explore the parking cruising behavior of commercial vehicle drivers in urban areas using widely available GPS data, and how urban transport infrastructure impacts parking cruising times.

We apply the method to a sample of 2900 trips performed by a fleet of commercial vehicles, delivering and picking up parcels in Seattle downtown. We obtain an average estimated parking cruising time of 2.3 minutes per trip, contributing on average for 28 percent of total trip time. We also found that cruising for parking decreased as more curb-space was allocated to commercial vehicles load zones and paid parking and as more off-street parking areas were available at trip destinations, whereas it increased as more curb space was allocated to bus zone.

Recommended Citation:
Dalla Chiara, Giacomo, & Goodchild, Anne. (2020) Do Commercial Vehicles Cruise for Parking? Empirical Evidence from Seattle. Transport Policy, 97, 26-36. https://doi.org/10.1016/j.tranpol.2020.06.013